






import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_PRETRAINING_MAPPING,
      NezhaForMaskedLM,
      NezhaForMultipleChoice,
      NezhaForNextSentencePrediction,
      NezhaForPreTraining,
      NezhaForQuestionAnswering,
      NezhaForSequenceClassification,
      NezhaForTokenClassification,
      NezhaModel,
  )
  from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			int       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			int=13       ,				lowerCamelCase		:			Union[str, Any]=7       ,				lowerCamelCase		:			Optional[Any]=True       ,				lowerCamelCase		:			Dict=True       ,				lowerCamelCase		:			List[Any]=True       ,				lowerCamelCase		:			Optional[Any]=True       ,				lowerCamelCase		:			List[str]=99       ,				lowerCamelCase		:			Tuple=32       ,				lowerCamelCase		:			Any=5       ,				lowerCamelCase		:			Any=4       ,				lowerCamelCase		:			Optional[int]=37       ,				lowerCamelCase		:			Optional[Any]="gelu"       ,				lowerCamelCase		:			Dict=0.1       ,				lowerCamelCase		:			Any=0.1       ,				lowerCamelCase		:			Tuple=128       ,				lowerCamelCase		:			Dict=32       ,				lowerCamelCase		:			int=16       ,				lowerCamelCase		:			Dict=2       ,				lowerCamelCase		:			Optional[int]=0.02       ,				lowerCamelCase		:			Any=3       ,				lowerCamelCase		:			List[str]=4       ,				lowerCamelCase		:			Dict=None       ,				)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    seq_length
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_input_mask
         _UpperCAmelCase							=    use_token_type_ids
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    vocab_size
         _UpperCAmelCase							=    hidden_size
         _UpperCAmelCase							=    num_hidden_layers
         _UpperCAmelCase							=    num_attention_heads
         _UpperCAmelCase							=    intermediate_size
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    hidden_dropout_prob
         _UpperCAmelCase							=    attention_probs_dropout_prob
         _UpperCAmelCase							=    max_position_embeddings
         _UpperCAmelCase							=    type_vocab_size
         _UpperCAmelCase							=    type_sequence_label_size
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    num_labels
         _UpperCAmelCase							=    num_choices
         _UpperCAmelCase							=    scope


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size							)

         _UpperCAmelCase							=    None
         if self.use_input_mask:
                _UpperCAmelCase							=    random_attention_mask([self.batch_size, self.seq_length]							)

         _UpperCAmelCase							=    None
         if self.use_token_type_ids:
                _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				self.type_vocab_size							)

         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None
         if self.use_labels:
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.type_sequence_label_size							)
                _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				self.num_labels							)
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.num_choices							)

         _UpperCAmelCase							=    self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


  def  lowerCamelCase       (      self		:			Any							)						->	Any:


         """simple docstring"""
         return NezhaConfig(
             vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				hidden_act=self.hidden_act       ,				hidden_dropout_prob=self.hidden_dropout_prob       ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,				max_position_embeddings=self.max_position_embeddings       ,				type_vocab_size=self.type_vocab_size       ,				is_decoder=lowerCamelCase       ,				initializer_range=self.initializer_range       ,				)


  def  lowerCamelCase       (      self		:			int							)						->	List[str]:


         """simple docstring"""
         (
             (
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			
         )							=    self.prepare_config_and_inputs()

         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
         _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				vocab_size=2							)

         return (
             config,
             input_ids,
             token_type_ids,
             input_mask,
             sequence_labels,
             token_labels,
             choice_labels,
             encoder_hidden_states,
             encoder_attention_mask,
         )


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Tuple							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    NezhaModel(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase							)
         _UpperCAmelCase							=    model(lowerCamelCase       ,				token_type_ids=lowerCamelCase							)
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)							)
         self.parent.assertEqual(result.pooler_output.shape       ,				(self.batch_size, self.hidden_size)							)


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			List[Any]       ,				)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    NezhaModel(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase       ,				encoder_hidden_states=lowerCamelCase       ,				encoder_attention_mask=lowerCamelCase       ,				)
         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase       ,				encoder_hidden_states=lowerCamelCase       ,				)
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase							)
         self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)							)
         self.parent.assertEqual(result.pooler_output.shape       ,				(self.batch_size, self.hidden_size)							)


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Tuple							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    NezhaForMaskedLM(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.vocab_size)							)


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Optional[Any]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    NezhaForNextSentencePrediction(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase       ,				labels=lowerCamelCase       ,				)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, 2)							)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Any							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    NezhaForPreTraining(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase       ,				labels=lowerCamelCase       ,				next_sentence_label=lowerCamelCase       ,				)
         self.parent.assertEqual(result.prediction_logits.shape       ,				(self.batch_size, self.seq_length, self.vocab_size)							)
         self.parent.assertEqual(result.seq_relationship_logits.shape       ,				(self.batch_size, 2)							)


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Optional[int]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    NezhaForQuestionAnswering(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase       ,				start_positions=lowerCamelCase       ,				end_positions=lowerCamelCase       ,				)
         self.parent.assertEqual(result.start_logits.shape       ,				(self.batch_size, self.seq_length)							)
         self.parent.assertEqual(result.end_logits.shape       ,				(self.batch_size, self.seq_length)							)


  def  lowerCamelCase       (      self		:			int       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			str							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    self.num_labels
         _UpperCAmelCase							=    NezhaForSequenceClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)							)


  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			List[str]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.num_labels
         _UpperCAmelCase							=    NezhaForTokenClassification(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.num_labels)							)


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.num_choices
         _UpperCAmelCase							=    NezhaForMultipleChoice(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    input_ids.unsqueeze(1							).expand(-1       ,				self.num_choices       ,				-1							).contiguous()
         _UpperCAmelCase							=    token_type_ids.unsqueeze(1							).expand(-1       ,				self.num_choices       ,				-1							).contiguous()
         _UpperCAmelCase							=    input_mask.unsqueeze(1							).expand(-1       ,				self.num_choices       ,				-1							).contiguous()
         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				token_type_ids=lowerCamelCase       ,				labels=lowerCamelCase       ,				)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_choices)							)





  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.prepare_config_and_inputs()
         (
             (
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			
         )							=    config_and_inputs
         _UpperCAmelCase							=    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
         return config, inputs_dict



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (
      (
          NezhaModel,
          NezhaForMaskedLM,
          NezhaForMultipleChoice,
          NezhaForNextSentencePrediction,
          NezhaForPreTraining,
          NezhaForQuestionAnswering,
          NezhaForSequenceClassification,
          NezhaForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  _lowerCamelCase    =  (
      {
          '''feature-extraction''': NezhaModel,
          '''fill-mask''': NezhaForMaskedLM,
          '''question-answering''': NezhaForQuestionAnswering,
          '''text-classification''': NezhaForSequenceClassification,
          '''token-classification''': NezhaForTokenClassification,
          '''zero-shot''': NezhaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  _lowerCamelCase    =  True


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Union[str, Any]=False							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    super()._prepare_for_class(lowerCamelCase       ,				lowerCamelCase       ,				return_labels=lowerCamelCase							)

         if return_labels:
                if model_class in get_values(lowerCamelCase							):
                       _UpperCAmelCase							=    torch.zeros(
                           (self.model_tester.batch_size, self.model_tester.seq_length)       ,				dtype=torch.long       ,				device=lowerCamelCase							)
                       _UpperCAmelCase							=    torch.zeros(
                           self.model_tester.batch_size       ,				dtype=torch.long       ,				device=lowerCamelCase							)
         return inputs_dict


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    NezhaModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				hidden_size=37							)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	int:


         """simple docstring"""
         self.config_tester.run_common_tests()


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	str:


         """simple docstring"""
         # This regression test was failing with PyTorch < 1.3
         (
             (
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			
         )							=    self.model_tester.prepare_config_and_inputs_for_decoder()

         _UpperCAmelCase							=    None

         self.model_tester.create_and_check_model_as_decoder(
             lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				)


  def  lowerCamelCase       (      self		:			List[str]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Any							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			List[str]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_next_sequence_prediction(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Dict							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			List[str]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCamelCase							)


  @slow
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Optional[Any]:


         """simple docstring"""
         for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase							=    NezhaModel.from_pretrained(lowerCamelCase							)
                self.assertIsNotNone(lowerCamelCase							)





  @slow
  @require_torch_gpu
  def  lowerCamelCase       (      self		:			Tuple							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
                # NezhaForMultipleChoice behaves incorrectly in JIT environments.
                if model_class == NezhaForMultipleChoice:
                       return

                _UpperCAmelCase							=    True
                _UpperCAmelCase							=    model_class(config=lowerCamelCase							)

                _UpperCAmelCase							=    self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase							)
                _UpperCAmelCase							=    torch.jit.trace(
                    lowerCamelCase       ,				(inputs_dict["""input_ids"""].to("""cpu"""							), inputs_dict["""attention_mask"""].to("""cpu"""							))							)

                with tempfile.TemporaryDirectory() as tmp:
                       torch.jit.save(lowerCamelCase       ,				os.path.join(lowerCamelCase       ,				"""bert.pt"""							)							)
                       _UpperCAmelCase							=    torch.jit.load(os.path.join(lowerCamelCase       ,				"""bert.pt"""							)       ,				map_location=lowerCamelCase							)
                       loaded(inputs_dict["""input_ids"""].to(lowerCamelCase							)       ,				inputs_dict["""attention_mask"""].to(lowerCamelCase							)							)



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  @slow
  def  lowerCamelCase       (      self		:			Dict							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    NezhaModel.from_pretrained("""sijunhe/nezha-cn-base"""							)
         _UpperCAmelCase							=    torch.tensor([[0, 1, 2, 3, 4, 5]]							)
         _UpperCAmelCase							=    torch.tensor([[0, 1, 1, 1, 1, 1]]							)
         with torch.no_grad():
                _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase							)[0]
         _UpperCAmelCase							=    torch.Size((1, 6, 768)							)
         self.assertEqual(output.shape       ,				lowerCamelCase							)
         _UpperCAmelCase							=    torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]]							)

         self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,				lowerCamelCase       ,				atol=1E-4							)							)





  @slow
  def  lowerCamelCase       (      self		:			str							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base"""							)
         _UpperCAmelCase							=    torch.tensor([[0, 1, 2, 3, 4, 5]]							)
         _UpperCAmelCase							=    torch.tensor([[1, 1, 1, 1, 1, 1]]							)
         with torch.no_grad():
                _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase							)[0]
         _UpperCAmelCase							=    torch.Size((1, 6, 2_1128)							)
         self.assertEqual(output.shape       ,				lowerCamelCase							)
         _UpperCAmelCase							=    torch.tensor(
             [[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]]							)

         self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,				lowerCamelCase       ,				atol=1E-4							)							)
"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   float:




						'''simple docstring'''



						return price * (1 + tax_rate)


if __name__ == "__main__":
	print(F'''{price_plus_tax(100, 0.25) = }''')
	print(F'''{price_plus_tax(125.50, 0.05) = }''')


